Summary: Exploiting Repetitive Object Patterns for Model
Compression and Completion
Luciano Spinello1,2, Rudolph Triebel2, Dizan Vasquez3,
Kai O. Arras1, and Roland Siegwart2
1 Social Robotics Lab, University of Freiburg, Germany
2 Autonomous Systems Lab, ETH Zurich, Switzerland
3 ITESM Campus Cuernavaca, Mexico
Abstract. Many man-made and natural structures consist of similar elements
arranged in regular patterns. In this paper we present an unsupervised approach
for discovering and reasoning on repetitive patterns of objects in a single image.
We propose an unsupervised detection technique based on a voting scheme of
image descriptors. We then introduce the concept of latticelets: minimal sets of
arcs that generalize the connectivity of repetitive patterns. Latticelets are used for
building polygonal cycles where the smallest cycles define the sought groups of
repetitive elements. The proposed method can be used for pattern prediction and
completion and high-level image compression. Conditional Random Fields are
used as a formalism to predict the location of elements at places where they are
partially occluded or detected with very low confidence. Model compression is
achieved by extracting and efficiently representing the repetitive structures in the
image. Our method has been tested on simulated and real data and the quantitative